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dc.contributor.authorMoridian, Parisa
dc.contributor.authorGhassemi, Navid
dc.contributor.authorJafari, Mahboobeh
dc.contributor.authorSalloum-Asfar, Salam
dc.contributor.authorSadeghi, Delaram
dc.contributor.authorKhodatars, Marjane
dc.contributor.authorShoeibi, Afshin
dc.contributor.authorKhosravi, Abbas
dc.contributor.authorLing, Sai Ho
dc.contributor.authorSubasi, Abdulhamit
dc.contributor.authorAbdulla, Sara
dc.contributor.authorAlizadehsani, Roohallah
dc.contributor.authorGorriz, Juan
dc.contributor.authorAcharya, U Rajendra
dc.date.accessioned2023-03-13T07:11:07Z
dc.date.available2023-03-13T07:11:07Z
dc.date.issued2022
dc.identifier.citationMoridian, et. al., Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review, Frontiers in Molecular Neuroscience, Volume 15, 2022en_US
dc.identifier.doihttps://doi.org/10.3389/fnmol.2022.999605en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/600
dc.description.abstractAutism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.en_US
dc.subjectAutism Spectrum Disorderen_US
dc.subjectMagnetic Resonance Imagingen_US
dc.subjectArtificial Intelligence (AI)en_US
dc.subjectMachine Learningen_US
dc.titleAutomatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimag-ing: A reviewen_US
dc.source.journalFrontiers in Molecular Neuroscienceen_US
dc.source.volume15en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.labArtificial Intelligence & Cyber Security Laben_US
dc.subject.KSAHEALTHen_US
dc.source.indexScopusen_US
dc.source.indexWoSen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.firstauthorMoridian, Parisa


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